Model Complexity and Computational Costs. We keep same number of transformer blocks in Table REF  to clearly compare different patch embeddings. The difference in computational complexity between ICS and convolution stem is negligible. [1]} has shown that the convolution stem has approximately the same complexity as a single transformer block, and adding the convolution stem while removing one transformer block can maintain similar model complexity without affecting accuracy. To further verify this point, extra experiments are conducted by removing one transformer block in the CFS-based supervised setting ( ViT\(_I\) -S/16 in “CFS” section of Table REF ), we achieve 91.2% mAP and 96.1% rank-1 accuracy on Market, almost the same as the numbers in Table REF . Therefore, it is reasonable to expect that the performance of our other experiments will be unchanged if we remove a transformer block to maintain the complexity comparable to the vanilla ViT models.
